In digital image processing it is often useful to detect the areas in an image that are human eyes. This information is used for example, to locate other features in the image relative to the eyes, or to find the orientation of a human face in the image. U.S. Pat. No. 6,072,892 issued Jun. 6, 2000 to Kim discloses a method for detecting the position of eyes in a facial image using a simple thresholding method on an intensity histogram of the image to find three peaks in the histogram representing skin, white of the eye, and pupil.
One of the problems with this approach is that it needs to scan the entire image, pixel by pixel, and position a search window at each pixel that is not only unnecessary in consuming enormous computing power, but also it may produce a high rate of false positives because of similar histogram patterns that occur in places other than eye regions.
A neural networks method of locating human eyes is disclosed in Learning and Example Selection for Object and Pattern Detection, A.I.T.R. No. 1572, MIT, by Kah-Kay Sung, January, 1996. This method discloses training a neural network to recognize eyes with acceptable distortion from a pre-selected eye template. The operator repeatedly distorts the original eye template and all variations produced from distorting eyes are labeled as either acceptable or unacceptable. The distorted samples, i.e., the training images, and the associated labeling information are fed to the neural network. This training process is repeated until the neural network has achieved satisfactory recognition performance for the training images. The trained neural network effectively has stored a plurality of possible variations of the eye. Locating an eye is done by feeding a region in the image to the neural network for determining if a desired output, i.e., a match, occurs; all matches are identified as eyes.
Although the presently known and utilized methods of identifying eyes are satisfactory, they are not without drawbacks. The touch screen method requires constant human interaction of repeatedly touching the touch screen for zooming in on the eye and, as a result, is somewhat labor intensive. Still further, the neural network method requires extensive training, and also exhaustive search to be performed for all the possible sizes and orientations of the eye. A method disclosed by Luo et al. (see U.S. Pat. No. 5,892,837, issued Apr. 6, 1999) improves the method of locating eyes in an image so as to overcome the above-described drawbacks. In Luo's method, the search of the eye position starts with two approximate locations provided by the user. In some applications, it is more desirable to have completely automatic eye positioning mechanism.
There is a need therefore for an improved method of utilizing other information embedded in a digital facial image to locate human eyes in a completely automatic, yet computationally efficient manner.